Dataset for whole pointclouds with farthest point sampling _incomplete_

_templates/new_project/datasets/template_dataset.py

@@ -3,4 +3,10 @@ from torch.utils.data import Dataset
 
 class TemplateDataset(Dataset):
     def __init__(self, *args, **kwargs):
-        super(TemplateDataset, self).__init__()
+        super(TemplateDataset, self).__init__()
+
+    def __len__(self):
+        pass
+
+    def __getitem__(self, item):
+        return item

point_toolset/sampling.py

@@ -0,0 +1,24 @@
+import numpy as np
+
+
+class FarthestpointSampling():
+
+    def __init__(self, K):
+        self.k = K
+
+    def __call__(self, pts, *args, **kwargs):
+
+        if pts.shape[0] < self.k:
+            return pts
+
+        def calc_distances(p0, points):
+            return ((p0[:3] - points[:, :3]) ** 2).sum(axis=1)
+
+        farthest_pts = np.zeros((self.k, pts.shape[1]))
+        farthest_pts[0] = pts[np.random.randint(len(pts))]
+        distances = calc_distances(farthest_pts[0], pts)
+        for i in range(1, self.k):
+            farthest_pts[i] = pts[np.argmax(distances)]
+            distances = np.minimum(distances, calc_distances(farthest_pts[i], pts))
+
+        return farthest_pts